Learning methods for RSSI-based geolocation: A comparative study

Abstract

In this paper, we investigate machine learning approaches addressing the problem of geolocation. First, we review some classical learning methods to build a radio map. In particular, these methods are splitted in two categories, which we refer to as likelihood-based methods and fingerprinting methods. Then, we provide a novel geolocation approach in each of these two categories. The first proposed technique relies on a semi-parametric Nadaraya-Watson estimator of the likelihood, followed by a maximum a posteriori (MAP) estimator of the object's position. The second technique consists in learning a proper metric on the dataset, constructed by means of a Gradient boosting regressor: a k-nearest neighbor algorithm is then used to estimate the position. Finally, all the proposed methods are compared on a data set originated from Sigfox network. The experiments show the interest of the proposed methods, both in terms of location estimation performance, and of ability to build radio maps.

Detecting Unauthorized Movement of Radioactive Material Packages in Transport with an Adam-Optimized BP Neural Network Model

“…Tis method efectively reduces the errors caused by small probability events and improves the accuracy of the measured data. Based on empirical observations, RSSI values follow a Gaussian distribution [31], with the probability density function in the form as follows:…”